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Deep learning (DL) has recently changed the development of intelligent systems and is widely adopted in many real-life applications. Despite their various benefits and potentials, there is a high demand for DL processing in different computationally limited and energy-constrained devices. It is natural to study game-changing technologies such as Binary Neural Networks (BNN) to increase DL capabilities. Recently remarkable progress has been made in BNN since they can be implemented and embedded on tiny restricted devices and save a significant amount of storage, computation cost, and energy consumption. However, nearly all BNN acts trade with extra memory, computation cost, and higher performance. This article provides a complete overview of recent developments in BNN. This article focuses exclusively on 1-bit activations and weights 1-bit convolution networks, contrary to previous surveys in which low-bit works are mixed in. It conducted a complete investigation of BNN’s development—from their predecessors to the latest BNN algorithms/techniques, presenting a broad design pipeline and discussing each module’s variants. Along the way, it examines BNN (a) purpose: their early successes and challenges; (b) BNN optimization: selected representative works that contain essential optimization techniques; (c) deployment: open-source frameworks for BNN modeling and development; (d) terminal: efficient computing architectures and devices for BNN and (e) applications: diverse applications with BNN. Moreover, this paper discusses potential directions and future research opportunities in each section.

The intrinsic features of documents, such as paper color, texture, aging, translucency, the kind of printing, typing or handwriting, etc., are important with regard to how to process and enhance their image. Image binarization is the process of producing a monochromatic image having its color version as input. It is a key step in the document processing pipeline. The recent Quality-Time Binarization Competitions for documents have shown that no binarization algorithm is good for any kind of document image. This paper uses a sample of the texture of the scanned historical documents as the main document feature to select which of the 63 widely used algorithms, using five different versions of the input images, totaling 315 document image-binarization schemes, provides a reasonable quality-time trade-off.

Purpose: To compare the accuracy of manual and automated binarization technique for the analysis of choroidal vasculature. Methods: This retrospective study was performed on a total of 98 eyes of 60 healthy subjects. Fovea-centered swept source optical coherence tomography (SS-OCT) scans were obtained and choroidal area was binarized using manual and automated image binarization technique separately. Choroidal vessel visualization in the binarized scans were subjectively graded (grades 0-100) by comparing them with the original OCT scan images by two masked graders. The subjective variability and repeatability was compared between two binarization method groups. Intergrader and intragrader variability was estimated using paired t-test. The degree of agreement between the grades for each observer and between the observers was evaluated using Bland-Altman plot. Results: The mean accuracy grades of the automatically binarized images were significantly (P < 0.001) higher (93.38% ± 1.70%) than that of manually binarized images (78.06% ± 2.92%). There was a statistically significant variability and poor agreement between the mean interobserver grades in the manual binarization arm. Conclusion: Automated image binarization technique is faster and appears to be more accurate in comparison to the manual method.

High-resolution peripheral quantitative computed tomography (HR-pQCT) has emerged as a powerful imaging technique for characterizing bone microarchitecture in the human peripheral skeleton. The second-generation HR-pQCT scanner provides improved spatial resolution and a shorter scan time. However, the transition from the first-generation (XCTI) to second-generation HR-pQCT scanners (XCTII) poses challenges for longitudinal studies, multi-center trials, and comparison to historical data. Cross-calibration, an established approach for determining relationships between measurements obtained from different devices, can bridge this gap and enable the utilization and comparison of legacy data. The goal of this study was to establish cross-calibration equations to estimate XCTII measurements from XCTI data, using both the standard and Laplace-Hamming (LH) binarization approaches. Thirty-six volunteers (26–85 yr) were recruited and their radii and tibiae were scanned on both XCTI and XCTII scanners. XCTI images were analyzed using the manufacturer’s standard protocol. XCTII images were analyzed twice: using the manufacturer’s standard protocol and the LH segmentation approach previously developed and validated by our team. Linear regression analysis was used to establish cross-calibration equations. Results demonstrated strong correlations between XCTI and XCTII density and geometry outcomes. For most microstructural outcomes, although there were considerable differences in absolute values, correlations between measurements obtained from different scanners were strong, allowing for accurate cross-calibration estimations. For some microstructural outcomes with a higher sensitivity to spatial resolution (eg, trabecular thickness, cortical pore diameter), XCTII standard protocol resulted in poor correlations between the scanners, while our LH approach improved these correlations and decreased the difference in absolute values and the proportional bias for other measurements. For these reasons and due to the improved accuracy of our LH approach compared with the standard approach, as established in our previous study, we propose that investigators should use the LH approach for analyzing XCTII scans, particularly when comparing to XCTI data.

The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the initial stages. Computed tomography (CT) imaging aids specialists in detecting various kidney conditions. The research focuses on classifying CT images of cysts, normal states, stones, and tumors using a hyperparameter fine-tuned approach with convolutional neural networks (CNNs), VGG16, ResNet50, CNNAlexnet, and InceptionV3 transfer learning models. It introduces an innovative methodology that integrates finely tuned transfer learning, advanced image processing, and hyperparameter optimization to enhance the accuracy of kidney tumor classification. By applying these sophisticated techniques, the study aims to significantly improve diagnostic precision and reliability in identifying various kidney conditions, ultimately contributing to better patient outcomes in medical imaging. The methodology implements image-processing techniques to enhance classification accuracy. Feature maps are derived through data normalization and augmentation (zoom, rotation, shear, brightness adjustment, horizontal/vertical flip). Watershed segmentation and Otsu’s binarization thresholding further refine the feature maps, which are optimized and combined using the relief method. Wide neural network classifiers are employed, achieving the highest accuracy of 99.96% across models. This performance positions the proposed approach as a high-performance solution for automatic and accurate kidney CT image classification, significantly advancing medical imaging and diagnostics. The research addresses the pressing need for early kidney disease detection using an innovative methodology, highlighting the proposed approach’s capability to enhance medical imaging and diagnostic capabilities.

Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.

Binary neural network (BNN) is an effective approach to accelerate the model inference and has been initially applied in the field of single image super resolution (SISR). However, the optimization of efficiency and accuracy remains a major challenge for achieving further improvements. While existing BNN-based SR methods solve the SISR problems by proposing a residual block-oriented quantization mechanism, the quantization process in the up-sampling stage and the representation tendency of binary super resolution networks are ignored. In this paper, we propose an Advanced Binary Super Resolution (ABSR) method to optimize the binary generator in terms of quantization mechanism and up-sampling strategy. Specifically, we first design an excitation-selection mechanism for binary inference, which could distinctively implement self-adjustment of activation and significantly reduce inference errors. Furthermore, we construct a binary up-sampling strategy that achieves performance almost equal to that of real-valued up-sampling modules, and fully frees up the inference speed of the binary network. Extensive experiments show that the ABSR not only reaches state-of-the-art BNN-based SR performance in terms of objective metrics and visual quality, but also reduces computational consumption drastically.

Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component’s identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.

Background Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites. Results The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84. Conclusions This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

Integrated information theory (IIT) provides a mathematical framework to characterize the cause-effect structure of a physical system and its amount of integrated information (Φ). An accompanying Python software package (“PyPhi”) was recently introduced to implement this framework for the causal analysis of discrete dynamical systems of binary elements. Here, we present an update to PyPhi that extends its applicability to systems constituted of discrete, but multi-valued elements. This allows us to analyze and compare general causal properties of random networks made up of binary, ternary, quaternary, and mixed nodes. Moreover, we apply the developed tools for causal analysis to a simple non-binary regulatory network model (p53-Mdm2) and discuss commonly used binarization methods in light of their capacity to preserve the causal structure of the original system with multi-valued elements.